Gabriele Di Antonio

Oct 22, 2024 | biorxiv.org | Gabriele Di Antonio |Tommaso Gili |Andrea Gabrielli |Maurizio Mattia

AbstractExploring the dynamics of a complex system, such as the human brain, poses significant challenges due to inherent uncertainties and limited data. In this study, we enhance the capabilities of noisy linear recurrent neural networks (lRNNs) within the reservoir computing framework, demonstrating their effectiveness in creating autonomous in silico replicas - digital twins - of brain activity.

Sep 18, 2024 | nature.com | Gabriele Di Antonio |Maurizio Mattia

AbstractA general mathematical description of how the brain sequentially encodes knowledge remains elusive. We propose a linear solution for serial learning tasks, based on the concept of mixed selectivity in high-dimensional neural state spaces. In our framework, neural representations of items in a sequence are projected along a “geometric” mental line learned through classical conditioning.